Skip to main content

Multispectral UAV Imagery Workshop

This workshop will focus on processing multispectral imagery from unoccupied aerial vehicles (UAVs) and extracting zonal statistics for geospatial modeling using OpenDroneMap on SCINet systems. Participants will be introduced to the different stages of processing multispectral imagery collected using UAVs, and the development of tools for the extraction of zonal statistics in a format that can be directly used in geospatial modeling will be shared. Topics covered in the workshop will include:

  • Installation and set up of the miniconda environment
  • Pre-processing: Tools to filter out redundant imagery by altitude and location
  • Pre-processing: Conversion of multispectral imagery digital numbers to reflectance
  • Processing: OpenDroneMap to generate multispectral orthophotos
  • Tools to generate vegetation indices from multispectral imagery, volume estimates from digital surface models, and extraction of zonal statistics

Prerequisites: Workshop participants should have some familiarity with Python, using the command line, and basic knowledge about remote sensing and geospatial concepts.

To register for the Multispectral UAV Imagery Workshop, please fill out this form.

Leads:

  • Alexander Hernandez, Research Biologist (Computational), Forage and Range Research Unit
  • Efrain Duarte, SCINet/AI-COE Postdoctoral Fellow, Forage and Range Research Unit
  • Kaden Patten, Biological Science Technician, Forage and Range Research Unit

Pre-Workshop Instructions:

To help minimize technical issues and delays at the start of the workshop, please try the following three tests prior to the workshop.

  • Logging on to Atlas Open OnDemand (OOD): Please confirm you can successfully log in to Atlas OOD with your SCINet account (see instructions here). If you are successful, you will be able to see the Atlas OOD home page.
  • Atlas Shell Access: When on Atlas OOD, click on the top navigation bar: “Clusters” > “Atlas Shell Access”. A new tab will appear that looks like a shell terminal (e.g., like PowerShell). Please confirm you do not receive any error messages or requests to re-authenticate and that the final line looks like “[firstname.lastname@atlas-login-1 ~]$”.
  • JupyterLab Server: Back on the main Atlas OOD tab, click on the top or side navigation bar: “Interactive Apps” > “JupyterLab Server”.
    • Fill the input fields with the following (input fields not listed below can be left at their default values):
      • Partition: atlas
      • Number of hours: 1
      • Number of nodes: 1
      • Number of tasks: 1
      • Additional Slurm Parameters: (leave empty)
    • Click the “Launch” button.
    • Wait a moment for the job card to update from “Queued” to “Running”.
    • Please confirm that clicking on the “Connect to JupyterLab Server” button opens a new tab with the JupyterLab Server interface.

Tutorial Setup Instructions

Steps to prepare for the tutorial session each day:

  1. Login to Atlas Open OnDemand at https://atlas-ood.hpc.msstate.edu/. For more information on login procedures for web-based SCINet access, see the SCINet access user guide.

  2. Open a command-line session by clicking on “Clusters” -> “Atlas Shell Access” on the top menu. This will open a new tab with a command-line session on Atlas’s login node.

  3. Request resources on a compute node to avoid using the login node for data transfers by running the following command.

     srun --reservation=workshop2 -A scinet_workshop2 -t 00:30:00 -n 1 --mem 8G --pty bash

  4. Create a workshop working directory and copy the workshop materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the $USER variable.

     mkdir -p /90daydata/shared/$USER/
 cd /90daydata/shared/$USER/
 cp -r /project/scinet_workshop2/multispectral_UAV/detect_GCPs .
 cp -r /project/scinet_workshop2/multispectral_UAV/flight_filtering .
 cp -r /project/scinet_workshop2/multispectral_UAV/Workshop3 .

  5. Setup kernel for JupyterLab. In the workshop project space, there is a geospatial_conda conda environment for the packages we will be using during the workshop tutorials. You will create a kernel called uav_workshop to access from JupyterLab.

    First load the necessary module:

     module load miniconda3

    If you have not used conda on Atlas before, you will first need to run these commands:

     conda init
 conda config --set auto_activate_base false
 source ~/.bashrc

    For everyone, you will create a new kernelspec from the conda environment with these commands:

     conda activate /project/scinet_workshop2/multispectral_UAV/geospatial_conda
 ipython kernel install --name "uav_workshop" --user

  6. Stop the interactive job on the compute node by running the command exit.

  7. Launch a JupyterLab Server session. Under the Interactive Apps menu, select JupyterLab Server. Specify the following input values on the page:

    • Account: scinet_workshop2
    • Partition: atlas
    • QOS: normal 14-00:00:00
    • Number of hours: 4
    • Number of nodes: 1
    • Number of tasks: 16
    • Additional Slurm Parameters: --mem-per-cpu=6G --reservation=workshop2
    • Home Directory: /90daydata/shared/${USER}

    Click Launch. The screen will update to the Interactive Sessions page. When your Jupyter session is ready, the top card will update from Queued to Running and a Connect to Jupyter button will appear. Click Connect to Jupyter.

  8. For each notebook used in the tutorial, select the uav_workshop kernel.


    • October 9-10, 2024, 1-5 PM ET
      • Prerequisites:
        • Familiarity with the Python programming language
        • Experience with the command line will be very helpful for working through the exercises
        • Some basic knowledge about remote sensing (i.e., digital numbers vs reflectance, vegetation indices) and geospatial (i.e., zonal statistics) concepts will be beneficial.